Abstract: The present teachings include techniques for securing a connection to a dialyzer or the like—e.g., securing the junction between a dialyzer port and a connector (e.g., a DIN connector) that couples the port to tubing of an extracorporeal circuit of a hemodialysis system. To this end, a locking device may engage both a DIN connector and a portion of the dialyzer, such as the cap or an adapter engaged therewith. The locking device may include an interior void sized and shaped to accommodate winged portions (or other portions) of the DIN connector to mitigate rotation thereof relative to the port to which it is engaged. Further, the locking device may be used to ensure that coupling between the DIN connector and the port is proper and secure. In this manner, a locking device can mitigate leaks, which can be catastrophic during a hemodialysis treatment or the like.

Abstract: The present teachings include techniques for securing a connection to a dialyzer or the like—e.g., securing the junction between a dialyzer port and a connector (e.g., a DIN connector) that couples the port to tubing of an extracorporeal circuit of a hemodialysis system. To this end, a locking device may engage both a DIN connector and a portion of the dialyzer, such as the cap or an adapter engaged therewith. The locking device may include an interior void sized and shaped to accommodate winged portions (or other portions) of the DIN connector to mitigate rotation thereof relative to the port to which it is engaged. Further, the locking device may be used to ensure that coupling between the DIN connector and the port is proper and secure. In this manner, a locking device can mitigate leaks, which can be catastrophic during a hemodialysis treatment or the like.

Abstract: The present teachings include techniques for securing a connection to a dialyzer or the like—e.g., securing the junction between a dialyzer port and a connector (e.g., a DIN connector) that couples the port to tubing of an extracorporeal circuit of a hemodialysis system. To this end, a locking device may engage both a DIN connector and a portion of the dialyzer, such as the cap or an adapter engaged therewith. The locking device may include an interior void sized and shaped to accommodate winged portions (or other portions) of the DIN connector to mitigate rotation thereof relative to the port to which it is engaged. Further, the locking device may be used to ensure that coupling between the DIN connector and the port is proper and secure. In this manner, a locking device can mitigate leaks, which can be catastrophic during a hemodialysis treatment or the like.

Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises generating, via a processor, a set of hashes for each of a plurality of objects. The method also comprises computing, via the processor, a high-dimensional sparse vector for each object, where the vector represents the set of hashes for each object. The method further comprises computing, via the processor, a combined high-dimensional sparse vector from the high-dimensional sparse vectors for all objects and computing a hash suppression threshold. The method also comprises determining, via the processor, a group of hashes to be suppressed by using the hash suppression threshold, and suppressing, via the processor, the group of selected hashes when performing an action.

Abstract: Examples disclosed herein involve data stream analytics. In examples herein, a data stream may be analyzed by computing a set of hashes of a real-valued vector, the real-valued vector corresponding to a sample data object of a data stream; generating a list of data objects from a database corresponding to the sample data object based on the set of hashes, the list of data objects ordered based on similarity of the data objects to the sample data object of the data stream; and updating a data structure representative of activity of the sample data object in the data stream based on the list of data objects, the data structure to provide incremental analysis corresponding to the sample data object.

Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises generating, via a processor, a set of hashes for each of a plurality of objects. The method also comprises computing, via the processor, a high-dimensional sparse vector for each object, where the vector represents the set of hashes for each object. The method further comprises computing, via the processor, a combined high-dimensional sparse vector from the high-dimensional sparse vectors for all objects and computing a hash suppression threshold. The method also comprises determining, via the processor, a group of hashes to be suppressed by using the hash suppression threshold, and suppressing, via the processor, the group of selected hashes when performing an action.

Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises generating, via a processor, a set of hashes for each of a plurality of objects. The method also comprises computing, via the processor, a high-dimensional sparse vector for each object, where the vector represents the set of hashes for each object. The method further comprises computing, via the processor, a combined high-dimensional sparse vector from the high-dimensional sparse vectors for all objects and computing a hash suppression threshold. The method also comprises determining, via the processor, a group of hashes to be suppressed by using the hash suppression threshold, and suppressing, via the processor, the group of selected hashes when performing an action.

Abstract: Automatic selection of neighbor lists to be incrementally updated is disclosed. One example is a system including an indexing module to receive an incoming data stream, and retrieve neighbor lists for received data objects. An evaluator determines similarity measures between pairs of the received data objects. A threshold determination module determines distributions of order statistics based on the determined similarity measures and retrieved neighbor lists, and a threshold based on the distributions of order statistics. The evaluator determines additional similarity measures between a new data object in the data stream and the received data objects.

Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises generating, via a processor, a set of hashes for each of a plurality of objects. The method also comprises computing, via the processor, a high-dimensional sparse vector for each object, where the vector represents the set of hashes for each object. The method further comprises computing, via the processor, a combined high-dimensional sparse vector from the high-dimensional sparse vectors for all objects and computing a hash suppression threshold. The method also comprises determining, via the processor, a group of hashes to be suppressed by using the hash suppression threshold, and suppressing, via the processor, the group of selected hashes when performing an action.

Abstract: Dynamically updating a ridge regression data model of a continuous stream of data is disclosed. New data chunks corresponding to a current data accumulation point are received and the data values in the new data chunks are transformed via standardization methods. A ridge estimator for the standardized data that includes data chunks received up to a penultimate data accumulation point to include the new data chunks is dynamically updated. The cumulative observations received up to the current data accumulation point are updated and stored. Predictions for the continuous data stream are generated based on the updated ridge estimator.

Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises generating a base image representing a location of a wireless-enabled device with data from a plurality of wireless beacons and generating an image fingerprint for the location of the wireless-enabled device by using the base image. The method further comprises comparing the image fingerprint for the location of the wireless-enabled device with a plurality of existing image fingerprints associated with the plurality of wireless beacons, and determining a location of the wireless-enabled device based on the comparison.

Abstract: Automatic selection of neighbor lists to be incrementally updated is disclosed. One example is a system including an indexing module to receive an incoming data stream, and retrieve neighbor lists for received data objects. An evaluator determines similarity measures between pairs of the received data objects. A threshold determination module determines distributions of order statistics based on the determined similarity measures and retrieved neighbor lists, and a threshold based on the distributions of order statistics. The evaluator determines additional similarity measures between a new data object in the data stream and the received data objects.

Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises generating a base image representing a location of a wireless-enabled device with data from a plurality of wireless beacons and generating an image fingerprint for the location of the wireless-enabled device by using the base image. The method further comprises comparing the image fingerprint for the location of the wireless-enabled device with a plurality of existing image fingerprints associated with the plurality of wireless beacons, and determining a location of the wireless-enabled device based on the comparison.

Abstract: In some examples, a method includes accessing input vectors in an input space, wherein the input vectors characterize elements of a physical system. The method may also include generating feature vectors from the input vectors, and the feature vectors are generated without any vector product operations between performed between any of the input vectors. An inner product of a pair of the feature vectors may correlate to an implicit kernel for the pair of feature vectors, and the implicit kernel may approximate a Gaussian kernel within a difference threshold. The method may further include providing the feature vectors to an application engine for use in analyzing the elements of the physical system, other elements in the physical system, or a combination of both.

Abstract: Examples disclosed herein involve data stream analytics. In examples herein, a data stream may be analyzed by computing a set of hashes of a real-valued vector, the real-valued vector corresponding to a sample data object of a data stream; generating a list of data objects from a database corresponding to the sample data object based on the set of hashes, the list of data objects ordered based on similarity of the data objects to the sample data object of the data stream; and updating a data structure representative of activity of the sample data object in the data stream based on the list of data objects, the data structure to provide incremental analysis corresponding to the sample data object.

Abstract: Dynamically updating a ridge regression data model of a continuous stream of data is disclosed. New data chunks corresponding to a current data accumulation point are received and the data values in the new data chunks are transformed via standardization methods. A ridge estimator for the standardized data that includes data chunks received up to a penultimate data accumulation point to include the new data chunks is dynamically updated. The cumulative observations received up to the current data accumulation point are updated and stored. Predictions for the continuous data stream are generated based on the updated ridge estimator.